Network Connectivity, Commodities, and the Adoption of Heavy Axle Loading by Short-line Railroads in Canada
Bibliographic record
Abstract
The implementation of heavy axle loading (HAL) by North America's freight railroads has improved productivity for Class I railroads, but these benefits are not necessarily realized by short-line railroads. This paper examines the potential impact of HAL on short-line railroads in Canada by analyzing two factors: connectivity to the Class I network and commodity type. Connectivity between a short-line and the Class I system is assessed in terms of a four-tier hierarchy, depending on whether the short-line is physically connected to the Class I network, and if so, the manner in which this connection occurs. In Canada, over half of the short-line mileage is connected to a Class I mainline and is therefore likely to be impacted by IIAL implementation. Of this mileage, the majority is owned by rail transportation management holding companies, but local rail operators and government also own a substantial proportion. The densities of commodities hauled by a short-line also influences the economic viability of HAL implementation. Based on available data on railcar weights, volumetric capacities, and commodity densities, the analysis shows that certain commodity-railcar pairings shift between cube-out and weigh-out conditions when moving to a 286k railcar. Moreover, of the 11 commodity-railcar pairings examined in this paper, there are e ight pairings in which the railcar design density is closer to the commodity density under 286k compared to 263k loading,indicating a potential improvement in railcar productivity. Further research using more detailed operational data tor Canadian short-line railroads will add value to these results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".